A power grid reconstruction and investment optimization method

Abstract

The application discloses a power grid reconstruction and investment optimization method, and belongs to the technical field of power system planning and investment decision-making; the method comprises the following steps: performing line loss depth analysis on multi-source operation data in the unit of a transformer area, separating technical loss and management loss, and generating an abnormal feature set; performing multi-dimensional correlation diagnosis on the abnormal features and indexes such as power supply radius, equipment life and load rate, accurately locking the problem transformer area and specific lesions; generating a candidate reconstruction scheme for the problem, simulating loss reduction benefits based on historical line loss curves and equipment loss characteristics, and quantitatively outputting marginal benefit values; constructing a multi-factor evaluation model with the highest weight of marginal benefits, automatically sorting and generating a project library according to a comprehensive priority index; the application solves problems such as traditional decision-making relying on a single threshold and being unable to quantitatively compare investment returns, realizes a leap from extensive experience judgment to data-driven accurate decision-making, and ensures that power grid reconstruction investment funds are preferentially invested in the field with the highest loss reduction return.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and investment decision-making technology, and specifically relates to a method for power grid transformation and investment optimization. Background Technology

[0002] The accuracy of power grid upgrade investment decisions directly impacts the asset utilization efficiency of power supply companies. Currently, the industry primarily uses the following three methods to arrange upgrade investments: 1. Replacement is done in a one-size-fits-all manner based on the service life of the equipment. For example, if the regulations stipulate that a distribution transformer must be included in the renovation plan after 20 years of operation, regardless of its actual wear and tear or load level, this approach ignores the differences in wear caused by the operating environment and load characteristics of the equipment. As a result, equipment in good condition is often replaced prematurely, while equipment with high wear and tear but not yet due for the required service life continues to operate. 2. Relying on comprehensive line loss rate threshold management, typically setting a fixed value (e.g., 7%) for a transformer area's line loss rate to trigger an investigation or inclusion in the upgrade reserve; however, statistical line loss includes both technical losses such as transformer iron and copper losses and line resistance losses, as well as management losses such as electricity theft, metering device malfunctions, and record errors. Directly carrying out equipment or line upgrades without distinguishing between them may result in some high-loss transformer areas having a high proportion of management line losses, and after investing a lot of money to replace equipment, the line loss rate may not decrease significantly; while transformer areas with high technical line losses due to aging equipment or excessively long power supply radius may not receive timely investment because the statistical line loss has not exceeded the threshold. 3. Empirical allocation of investment by block: The total annual investment is divided into blocks by region or substation, and each region selects its own renovation targets. This method lacks quantitative calculation and horizontal comparison of the loss reduction potential of each specific substation renovation plan. It cannot prioritize limited funds to projects with the highest loss reduction per unit of investment, resulting in low overall marginal benefits. For example, a substation may have an actual technical line loss rate of 10% due to severe no-load losses of the distribution transformer, but the statistical line loss rate may be pushed up to 15% due to electricity theft. Another substation may have an aging line but a lighter load, with a stable line loss rate of 8.5%. Judging solely by the comprehensive line loss rate threshold, the former may be prioritized for investment. However, after replacing the transformer, the line loss rate may remain high because electricity theft has not been eliminated. At the same time, if both substations are included in the renovation, it is impossible to determine which should be prioritized based on the return on loss reduction, which may easily lead to a delay in the investment of high-return projects. To address the aforementioned issues, it is essential to develop a method for power grid transformation and investment optimization. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a power grid transformation and investment optimization method that utilizes multi-source operational data to conduct in-depth analysis of line losses in distribution areas, accurately diagnose problematic equipment and grid structures, quantify the marginal benefits of transformation schemes, and prioritize investment. This method can achieve precision in power grid transformation investment and maximize benefits.

[0004] The objective of this invention is achieved as follows: a method for power grid transformation and investment optimization, comprising the following steps: Step 1: Using the transformer substation as a unit, collect electrical parameters, equipment ledgers, load curves, and grid structure data within the substation. Calculate the theoretical line loss and the actual statistical line loss, and conduct a refined comparative analysis by substation, line, and phase to generate a set of abnormal line loss rate features. Step 2: Correlate the abnormal feature set of line loss rate with equipment operating years, load rate, fault records, and power supply radius indicators. Based on the preset diagnostic rules, accurately identify specific transformer areas and key lines with severe losses, aging equipment, or unreasonable structure, and form a hierarchical and classified problem list. Step 3: For each object in the problem list, formulate a renovation or reactive power compensation plan, calculate the investment cost required for the implementation of the plan and the expected power loss reduction benefit, and output the marginal benefit quantification value of each plan. Step four: Construct a multi-dimensional investment evaluation model that includes marginal benefits, operational risks, and load growth demands. Input all candidate solutions into the multi-dimensional investment evaluation model for weighted scoring, and automatically prioritize investments according to the scoring results to generate a project library that prioritizes investment in areas with the highest marginal benefits.

[0005] Preferably, step one further includes: decomposing the time series of line loss rate, separating the trend component and the periodic component, identifying abnormal fluctuation points to distinguish between technical line loss anomalies and management line loss anomalies, and marking the technical line loss anomalies into the line loss rate anomaly feature set.

[0006] Preferably, in step one, the refined comparative analysis process is as follows: using the measurement data of the electricity information collection system, the electricity consumption of the main meter of the transformer area and the individual meter of the user is calculated and verified, and high-loss transformer areas with line loss rates that continuously exceed the preset threshold and negative-loss transformer areas with negative line loss rates are identified.

[0007] Preferably, the preset threshold is a relative judgment standard for different regions and time periods, dynamically calculated based on the real-time data distribution of the regional transformer area.

[0008] Preferably, in step two, the correlation analysis is performed by assigning spatial coordinates to the transformer area network topology, equipment operating parameters, and line loss data, constructing a multi-dimensional dynamic profile of the transformer area, using a discriminant model to cluster the transformer area, and automatically filtering out high-loss clusters caused by excessively long power supply radius, high-loss clusters caused by transformer overload, and low-voltage high-loss clusters caused by reactive power shortage.

[0009] Preferably, in step two, the preset diagnostic rules include: When the power supply radius of a transformer substation exceeds a preset multiple of the benchmark value for the same type of transformer substation and the line loss rate shows a strong positive correlation with the load, it is diagnosed as an unreasonable line structure. When the maximum load rate of the distribution transformer continuously exceeds the heavy load threshold and the no-load loss deviation exceeds the preset ratio, or the service life exceeds the set value and the status value deteriorates, it is diagnosed as equipment aging.

[0010] Preferably, in step three, the method for calculating the marginal benefit quantification value is as follows: extract the historical line loss power curve within the coverage area of ​​the renovation plan, overlay the loss reduction rate curve after equipment replacement, simulate and calculate the total power reduction for the whole year, and then divide it by the total life cycle cost to obtain the marginal power reduction benefit per unit investment.

[0011] Preferably, in step four, the weighted scoring algorithm is as follows: the marginal benefit quantification value is used as the highest weight factor, and the operation risk level score, load growth rate score and policy safety coefficient score are superimposed to automatically calculate the comprehensive priority index of each transformer substation renovation plan.

[0012] Preferably, in step four, the multi-dimensional investment evaluation model can also dynamically adjust the weight of the load growth demand dimension according to seasonal load fluctuations, so as to increase the weighting coefficient of this dimension during peak summer or winter periods.

[0013] Due to the adoption of the above technical solution, the beneficial effects of the present invention are: (1) This invention takes the transformer area as the smallest unit, integrates multi-source operation data such as electricity consumption measurement, equipment ledger, load curve and network topology, performs time-division and phase-division refined line loss calculation, and effectively separates the technical loss and management loss in the statistical line loss. It completely changes the limitation of the traditional method of relying on a single threshold of comprehensive line loss rate for rough judgment, and can accurately identify the cause of loss, thereby avoiding misjudgment and providing an accurate and reliable data foundation for subsequent transformation investment; (2) This invention performs spatial correlation analysis on abnormal line loss characteristics with operating status indicators such as power supply radius, equipment operating years, load rate, three-phase imbalance, and historical fault records. Through preset diagnostic rules, it automatically clusters and accurately locates specific lesions such as severe loss, equipment aging, or unreasonable grid structure, and forms a hierarchical and classified problem list. This upgrades the traditional investigation and diagnosis that relies on manual experience to automated and accurate positioning based on multi-dimensional data cross-validation, which greatly improves the accuracy and efficiency of diagnosis. (3) This invention automatically generates candidate renovation schemes for the diagnosed problems. Based on the historical load and line loss curves and the technical parameters of the equipment to be replaced, it simulates the annual loss reduction benefits and calculates the ratio with the total life cycle cost. It outputs the marginal loss reduction benefits per unit investment of each scheme, and then constructs a multi-dimensional investment evaluation model with marginal benefits as the highest weight, which integrates operational risks and load growth demands. It automatically sorts and generates a project library, thereby transforming the investment return from a fuzzy qualitative estimate into a precise and comparable quantitative indicator. Through multi-factor weighted sorting, it ensures that every investment is prioritized to the transformer area with the highest loss reduction returns, the greatest operational risks, and the most urgent needs, thus maximizing the comprehensive benefits of power grid renovation investment. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the method flow of the present invention, showing the four steps from data acquisition to outputting the project library and the data flow direction.

[0015] Figure 2 This is a schematic diagram of the scoring dimensions of the multi-dimensional investment evaluation model of the present invention, showing the logical relationship between each scoring dimension. Detailed Implementation

[0016] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0017] like Figure 1 As shown, this invention provides a method for power grid transformation and investment optimization. The specific implementation process of this method is as follows: Step 1: Conduct in-depth analysis of line loss in the transformer area to form an abnormal feature set.

[0018] The system acquires daily frozen and 15-minute-level data on electricity consumption, voltage, and current from the main meter of the distribution area and individual electricity meters. It also obtains transformer model, capacity, commissioning date, design no-load and load loss parameters from the production management system, and the power supply radius, line type and length, and topology of the distribution area from the GIS system. The system calculates the daily and monthly theoretical line loss for each distribution area using the root mean square current method and the equivalent resistance method, and compares this with the actual statistical line loss during the same period. The line loss rate is calculated phase-by-phase, and the time series is decomposed into trend and periodic components. High-loss distribution areas with line loss rates consistently exceeding preset thresholds, negative-loss distribution areas with negative line loss rates, and distribution areas with abnormal fluctuations in line loss rates that do not match load changes are identified and uniformly marked as abnormal features, and included in the abnormal line loss rate feature set.

[0019] The preset threshold is a relative judgment standard dynamically calculated based on the real-time data distribution of transformer substations in the region, divided by area and time period, to achieve accurate positioning and avoid misjudging substations with high line loss as equipment problems. In specific implementation, statistical anomaly detection benchmarks can be used for setting, such as adding three times the standard deviation (u+3σ) to the average line loss rate of all substations in the region as the threshold; alternatively, national standards and management zoning thresholds can be used, setting different thresholds for substations with different power consumption characteristics according to national standards and management guidelines, such as 5-6% for urban / residential substations and about 10% for agricultural substations, and directly marking substations exceeding the conventional standard in combination with management requirements; alternatively, a deviation multiple of theoretical line loss can be used to set the threshold, calculating the theoretical line loss rate of each substation, comparing it with the actual line loss rate, and marking an anomaly when the actual line loss rate exceeds 1.5 to 2 times the theoretical line loss rate.

[0020] Step Two: Multidimensional Diagnosis and Precise Localization.

[0021] For each distribution area with concentrated abnormal characteristics, the equivalent service life, annual maximum load rate, three-phase imbalance, and number of faults in the past three years of its distribution transformers are extracted. Combined with the farthest power supply distance of the distribution area measured by GIS, a discrimination rule is used for diagnosis: when the power supply radius of the distribution area exceeds 1.5 times the benchmark value of the same type of distribution area in the same region, and the line loss rate shows a significant positive correlation with the increase of load, it is diagnosed as high line loss caused by unreasonable grid structure; when the maximum load rate of the distribution transformer continuously exceeds 80% and the deviation between the measured no-load loss and the factory value exceeds 15%, or the service life exceeds the threshold and the condition parameters such as oil chromatography deteriorate, it is diagnosed as equipment aging or serious loss; when the power factor is continuously lower than 0.85 and the voltage is low, it is diagnosed as high loss caused by reactive power deficit; the diagnosed problem distribution areas are divided into three levels: emergency, serious, and general according to the severity of loss and the scope of impact, forming a problem list.

[0022] Step 3: Generate targeted renovation plans and quantify marginal benefits.

[0023] Based on the diagnostic findings, candidate solutions are generated for each problematic transformer area. For example, for transformer areas with high line loss, a solution of cutting or thickening the conductors is proposed; for transformer areas with aging equipment, a solution of replacing the transformer with a high-efficiency transformer is proposed; and for transformer areas with reactive power deficits, a solution of installing intelligent capacitor banks is proposed. For each solution, the hourly load and line loss curves of the transformer area over the past year are extracted, and the loss characteristic curves of the new equipment after the upgrade are superimposed (e.g., the no-load loss of the new transformer decreases by 60%, and the load loss decreases by 15%) to simulate and calculate the annual reduction in power loss after the upgrade. At the same time, the total life-cycle cost is calculated based on the engineering volume and equipment purchase price. The marginal benefit of the solution is obtained by dividing the average annual power loss reduction revenue over the entire life-cycle by the equivalent annual investment cost, expressed in kWh / yuan or as a monetized net present value index.

[0024] Step 4: Build an evaluation model and prioritize investments.

[0025] Construct a multi-dimensional weighted scoring model, such as Figure 2 As shown, the model includes four dimensions: marginal benefit dimension (weight 50%), which directly substitutes the normalized marginal benefit quantification value from step three; operational risk dimension (weight 25%), which is a comprehensive score based on the problem level, the number of failures in the past three years, and the load rate; load growth demand dimension (weight 15%), which is based on the load growth rate of the transformer area in the previous year and the planned forecast value; and policy security dimension (weight 10%), which considers whether it supplies power to important users and whether it belongs to the area required by the government for renovation.

[0026] The weighted summation yields the comprehensive priority index for each option. All options are then sorted from highest to lowest based on their comprehensive priority index to generate a project pool. Subsequent funding is strictly allocated according to this ranking until the budget is exhausted, ensuring that every penny flows to the project with the highest marginal benefit at the current stage.

[0027] The above steps can be repeated: after implementation, continue to collect line loss data, monitor the effect of the transformation, and if the actual benefits deviate from expectations, adjust the diagnostic rules and evaluation model weights to form a continuously optimized dynamic decision-making closed loop.

[0028] In summary, this invention transforms the traditional decision-making process based on fuzzy experience into a precise investment decision-making method that is data-supported, quantifiable, and dynamically closed-loop, effectively improving the overall marginal benefits of power grid transformation investment and simultaneously achieving precision and maximizing the benefits of power grid transformation investment.

[0029] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A method for power grid transformation and investment optimization, characterized in that, Includes the following steps: Step 1: Using the transformer substation as a unit, collect electrical parameters, equipment ledgers, load curves, and grid structure data within the substation. Calculate the theoretical line loss and the actual statistical line loss, and conduct a refined comparative analysis by substation, line, and phase to generate a set of abnormal line loss rate features. Step 2: Correlate the abnormal feature set of line loss rate with equipment operating years, load rate, fault records, and power supply radius indicators. Based on the preset diagnostic rules, accurately identify specific transformer areas and key lines with severe losses, aging equipment, or unreasonable structure, and form a hierarchical and classified problem list. Step 3: For each object in the problem list, formulate a renovation or reactive power compensation plan, calculate the investment cost required for the implementation of the plan and the expected power loss reduction benefit, and output the marginal benefit quantification value of each plan. Step four: Construct a multi-dimensional investment evaluation model that includes marginal benefits, operational risks, and load growth demands. Input all candidate solutions into the multi-dimensional investment evaluation model for weighted scoring, and automatically prioritize investments according to the scoring results to generate a project library that prioritizes investment in areas with the highest marginal benefits.

2. The power grid transformation and investment optimization method according to claim 1, characterized in that: Step one further includes: decomposing the time series of line loss rate, separating the trend component and the periodic component, identifying abnormal fluctuation points to distinguish between technical line loss anomalies and management line loss anomalies, and marking the technical line loss anomalies into the line loss rate anomaly feature set.

3. The power grid transformation and investment optimization method according to claim 1 or 2, characterized in that: In step one, the refined comparative analysis process is as follows: using the measurement data of the electricity information collection system, the electricity consumption of the main meter of the transformer area and the individual meter of the user is calculated and verified, and high-loss transformer areas with line loss rates that continuously exceed the preset threshold and negative-loss transformer areas with negative line loss rates are identified.

4. The power grid transformation and investment optimization method according to claim 3, characterized in that: The preset threshold is a relative judgment standard for different regions and time periods, dynamically calculated based on the real-time data distribution of the regional transformer area.

5. The power grid transformation and investment optimization method according to claim 1 or 2, characterized in that: In step two, the correlation analysis method is as follows: assign spatial coordinates to the network topology of the transformer area, equipment operating parameters and line loss data, construct a multi-dimensional dynamic profile of the transformer area, use a discriminant model to cluster the transformer area, and automatically filter out high-loss clusters of lines caused by excessively long power supply radius, high-loss clusters of equipment caused by heavy transformer overload and low-voltage high-loss clusters caused by reactive power shortage.

6. The power grid transformation and investment optimization method according to claim 1 or 2, characterized in that: In step two, the preset diagnostic rules include: When the power supply radius of a transformer substation exceeds a preset multiple of the benchmark value for the same type of transformer substation and the line loss rate shows a strong positive correlation with the load, it is diagnosed as an unreasonable line structure. When the maximum load rate of the distribution transformer continuously exceeds the heavy load threshold and the no-load loss deviation exceeds the preset ratio, or the service life exceeds the set value and the status value deteriorates, it is diagnosed as equipment aging.

7. The power grid transformation and investment optimization method according to claim 1 or 2, characterized in that: In step three, the method for calculating the marginal benefit quantification value is as follows: extract the historical line loss power curve within the coverage area of ​​the renovation plan, superimpose the loss reduction rate curve after equipment replacement, simulate and calculate the total power reduction for the whole year, and then divide it by the total life cycle cost to obtain the marginal power reduction benefit per unit investment.

8. The power grid transformation and investment optimization method according to claim 1 or 2, characterized in that: In step four, the weighted scoring algorithm is as follows: the marginal benefit quantification value is used as the highest weight factor, and the operation risk level score, load growth rate score and policy safety coefficient score are superimposed to automatically calculate the comprehensive priority index of each transformer substation renovation plan.

9. The power grid transformation and investment optimization method according to claim 1 or 2, characterized in that: In step four, the multi-dimensional investment evaluation model can also dynamically adjust the weight of the load growth demand dimension according to seasonal load fluctuations, so as to increase the weighting coefficient of this dimension during peak summer or winter periods.

Images